Computational Training: running fastR

Great R resources

In general:

1. Advanced R
2. R for Data Science
3. RStudio Cheatsheets
4. Useful R packages

Covering this session’s topics

5. data.table
6. parallel

What is “fast code”?

1. Easy to write
   familiarity with code editor, libraries

2. Easy to understand
   structured, with consistent variable names, commented.

3. Easy to debug
   clear naming, DRY, tests.

4. Easy to run
   🏎️ (profiling, C++, using “optimized” code).

Variable naming

tmp <-  10
tmp1 <- tmp * 24

cases_per_hour <- 10
cases_per_day <- cases_per_hour * 24

Embrace functional programming I

Functions are first-class citizens in R

first-class-citizenship.R

f <- function(x){x^2}

lapply(1:10, f)

generator <- function(n=2){
    function(x){x^n}
}
cube <- generator(3)

list(one_function = f)

Embrace functional programming II

Rethink for,while loops; “apply” instead

Say you want to extract the \(R^2\) from three linear models with different predictors (or formulae).

formulae.R

formulae <- c(
    Sepal.Length ~ Sepal.Width,
    Sepal.Length ~ Petal.Length,
    Sepal.Length ~ Species
)

bad way

lm_results2 <- c()

for (formula in formulae) {
    fit <- lm(formula, data = iris)
    r2 <- summary(fit)$r.squared
    lm_results2 <- c(lm_results2, r2)
}

good way

extract_r2 <- function(formula) {
    fit <- lm(formula, data = iris)
    r2 <- summary(fit)$r.squared
    return(r2)
}

lm_results <- sapply(formulae, extract_r2)

side effects

exists("fit")

The parallel package

You can imagine wanting to run each of the apply/for loop iterations in parallel.

sockets (not on Windows)

library(parallel)
f <- function(i) {
    lme4::lmer(
        Petal.Width ~ . - Species + (1 | Species),
        data = iris)
}

system.time(save1 <- lapply(1:100, f))
##    user  system elapsed
##   2.048   0.019   2.084
system.time(save2 <- mclapply(1:100, f))
##    user  system elapsed
##   1.295   0.150   1.471

forking

num_cores <- detectCores()
cl <- makeCluster(num_cores)
system.time(save3 <- parLapply(cl, 1:100, f))
#    user  system elapsed 
#   0.198   0.044   1.032 
stopCluster(cl)

Introduction to data.table

• data.table is a package that extends the data.frame class.
• (Quicker) alternative to dplyr for large datasets.
• cheatsheet

all you need to know

dt[i, j, by]

• use the data.table called dt …
• subset it on the rows specified i…
• and manipulate columns with j…
• grouped according to by.

Basic Examples with iris

Example 1: Subsetting and Summarizing

library(data.table)
dt <- as.data.table(iris)
dt[Species == "setosa", mean(Sepal.Length)]

Example 2: Grouping and Aggregating

library(data.table)
dt <- as.data.table(iris)
dt[Species == "setosa", mean(Sepal.Length)]

Profiling

• Identify (and hopefully fix!) bottlenecks in your code.
• The profvis package is a good package to use for this purpose.

Profiling Example: Column Means

library(profvis)
library(data.table)
n <- 4e5
cols <- 150
data <- as.data.frame(x = matrix(rnorm(n * cols, mean = 5), ncol = cols))
data <- cbind(id = paste0("g", seq_len(n)), data)
dataDF <- as.data.table(data)
numeric_vars <- setdiff(names(data), "id")

profvis({
  means <- apply(data[, names(data) != "id"], 2, mean)
  means <- colMeans(data[, names(data) != "id"])
  means <- lapply(data[, names(data) != "id"], mean)
  means <- vapply(data[, names(data) != "id"], mean, numeric(1))
  means <- matrixStats::colMeans2(as.matrix(data[, names(data) != "id"]))
  means <- dataDF[, lapply(.SD, mean), .SDcols = numeric_vars]
})

Profiling